Using partial components to restore and use the concurrent validity of the Index of Readiness
Bibliographic record
Abstract
In the presence of correlations among the dimensions of psychometric tests with summated scales, it is sometimes difficult to use the scores on the dimensions to predict their effects on various responses of interest through ordinary or generalized regression models, which can serve as concurrent validations. We will use the Index of Readiness (IR) as a case study to describe a statistical procedure to address this problem. Our solution will allow us to propose an optimal strategy of care to increase the adherence of HIV patients to treatments, as measured by a health indicator, by improving their readiness. Even on established and well validated multidimensional scales the psychometric properties on samples other than the original or princeps sample are sometimes difficult to ascertain (DeVellis, 2011; McIntire &al., 2010; Spector, 1992). Moreover, in the presence of correlated dimensions, when one tries to put into action a certain concurrent or criterion based validity on an external measurement, small coefficients of determination of regression models will prevent the determination and validation of any expected valid regression models for a criterion. Even more troublesome, the usual valid indicators of reliability or internal validity of the scales are not a guaranty for the unidimensionality of any given presumed dimension.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".